Programmatic SEO Engine & 59-Test Python Workflow-as-Code Library
Production-grade pSEO pipeline combining OpenSearch keyword modeling, Gemini Vertex AI generation with schema validation, and automated WordPress publishing.
STAR Engineering Breakdown
01 · Situation & Problem
Manual editorial was unable to scale across hundreds of career pathways; Tallo needed a programmatic content engine grounded in labor-market data with zero hallucination.
02 · Technical Execution
Built a Python workflow-as-code library that compiles queue-driven n8n generators integrating Lightcast/OpenSearch data, priority scoring (Vol / (Diff + 1)), Vertex AI prompt contracts, and automated JSON-LD schema.
03 · Guardrail Incident
When an AI agent configured a generator cron to fire every minute—stalling production n8n for 30 minutes—reverted the state, purged duplicate drafts, and codified a 59-test Pytest suite enforcing schedule and schema contracts.
04 · Verified Impact
100+ data-backed career pages published in 9 weeks, 50K+ monthly impressions on #1 tracked guide (pos ~4), and 0 schema errors across 1,200+ n8n nodes.
def test_career_generator_contract(career_workflow, gemini_payload):
graph = WorkflowGraph.from_builder(career_workflow)
assert graph.cron_interval_minutes >= 60 # Prevents 1-min runaway
validated = CareerPathwaySchema.model_validate(gemini_payload)
assert validated.json_ld["@type"] == "FAQPage"
assert len(validated.faqs) >= 4Benchmark Table
| METRIC | MANUAL BASELINE | PROGRAMMATIC ENGINE |
|---|---|---|
| Generator Build Time | 3 weeks | 1–2 days |
| Schema Error Rate | 12% | 0% |
| Automated Test Coverage | 0 tests | 59 Pytest Suite |